Analysis Of High-throughput Data Research Articles

Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA.

The historically fragmented biomedical data ecosystem has moved towards harmonization under the findable, accessible, interoperable, and reusable (FAIR) data principles, creating more opportunities for cloud-based research. This shift is especially opportune for scientists across diverse domains interested in implementing creative, nonstandard computational analytic pipelines on large and varied datasets. However, executing custom cloud analyses may present difficulties, particularly for investigators lacking advanced computational expertise. Here, we present an accessible, streamlined approach for the cloud compute platform CAVATICA that offers a solution. We outline how we developed a custom workflow in the cloud, for analyzing whole genome sequences of case-parent trios to detect sex-specific genetic effects on orofacial cleft risk, which required several programming languages and custom software packages. The approach involves just three components: Docker to containerize software environments, tool creation for each analysis step, and a visual workflow editor to weave the tools into a Common Workflow Language (CWL) pipeline. Our approach should be accessible to any investigator with basic computational skills, is readily extended to implement any scalable high-throughput biomedical data analysis in the cloud, and is applicable to other commonly used compute platforms such as BioData Catalyst. We believe our approach empowers versatile data reuse and promotes accelerated biomedical discovery in a time of substantial FAIR data.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

Abstract Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies.

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events.

Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor-binding sites and protein domains. These elements involve in exon usage and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes. With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein-protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing. The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R.

MYO5B levels are linked to microRNAs and UNC45A in colorectal cancer

Background: Colorectal cancer (CRC) is currently the third most prevalent cancer worldwide and ranks as the second deadliest. Colon cancer is characterized by disruption in cellular structure and polarity, processes that are commonly regulated by cell traffcking. The molecular motor Myosin 5b (Myo5b) is a key component of intracellular traffcking in the intestinal epithelium. Myo5b moves along F-actin tracks to transport cargo to and from the apical membrane. When Myo5b function is impaired, it leads to significant changes in gastrointestinal epithelial cells, including hyper-proliferation, alterations in tight junction proteins, and reduced differentiation. For Myo5b to function properly it must be properly folded; a process regulated by the myosin chaperone Unc45a. Recent work demonstrates that Myo5b levels are decreased in gastric cancer and during the progression of colorectal tumors. However, the mechanism driving this altered Myo5b expression in colorectal cancer remains unknown. We hypothesize that microRNAs regulate the expression of Unc45a, which subsequently impacts Myo5b function, promoting colorectal cancer. Methods and Results: Analysis of high-throughput RNA-sequencing data from the Cancer Genome Atlas (TCGA) revealed hyper-methylation of the Myo5b gene in CRC tumors (n=286) compared to controls (n=41). Findings in gastric cancer have shown that hyper-methylation leads to the silencing of Myo5b gene expression. Consistent with these findings, we observe a significant reduction in Myo5b gene expression in CRC tumors. Immunostaining of CRC tumor arrays and analysis from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) demonstrated a corresponding decrease in Myo5b protein levels. For comparison, we examined Myo5b levels in normal human colonic organoids and the colon cancer cell lines T84,HT-29, HT29-MTX, HCT-8 and HCT-116. Using qPCR, we confirmed a significant reduction in Myo5b gene expression in cancer cells compared to human colonic organoids. We postulated that even if Myo5b was expressed in CRC, it might not function properly if Unc45a function was perturbed. Using the same databases, we found that while Unc45a gene expression levels remain unchanged, its protein levels were significantly reduced. Analysis of the miRMap data reveled multiple microRNAs that could target Unc45a. The top 10 Unc45a-targeting microRNAs were all found to be elevated in CRC compared to normal control tissue, suggesting that microRNAs may lead to reduced Unc45a protein levels. The functional importance of decreased Myo5b in CRC was underscored by the observation that CRC patients with lower levels of Myo5b had reduced survival compared to those CRC patients with higher levels of Myo5b. Additionally, the loss of Myo5b correlated with increased ribosome biogenesis, a key driver of tumor proliferation. Conclusions: These findings indicate that reduced Myo5b in CRC may be attributed to gene methylation and impaired protein folding by Unc45a. The loss of Myo5b holds significance, as it is associated with poorer prognosis and linked with cancer progression. National Diabetes and Digestive and Kidney Diseases Grant R43 DK109820, RO1 DK048370, KO1 DK121869, MUSC start up funds, Histochemical 2022 Cornerstone Grant, and NIDDK T32. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

PAX1 represses canonical Wnt signaling pathway and plays dual roles during endoderm differentiation

BackgroundPaired box 1 (PAX1) is a transcription factor and essential for the development of pharyngeal pouches-derived tissues, including thymus. PAX1 mutations are identified in Severe Combined Immunodeficiency (SCID) patients with Otofaciocervical Syndrome Type 2 (OTFCS2). However, despite the critical roles of PAX1 in embryonic development and diseases, detailed insights into its molecular mode of action are critically missing.MethodsThe repressing roles of PAX1 and SCID associated mutants on Wnt signaling pathway were investigated by luciferase reporter assays, qRT-PCR and in situ hybridization in HEK293FT, HCT116 cells and zebrafish embryos, respectively. Co-immunoprecipitation (co-IP) and western blotting assays were carried out to identify the molecular mechanisms underlying PAX1’s role on Wnt signaling pathway. hESC based endoderm differentiation, flow cytometry, high-throughput sequencing data analysis, and qRT-PCR assays were utilized to determine the roles of PAX1 during endoderm differentiation.ResultsHere, we show that PAX1 represses canonical Wnt signaling pathway in vertebrate cells. Mechanically, PAX1 competes with SUMO E3 ligase PIASy to bind to TCF7L2, thus perturbing TCF7L2 SUMOylation level, further reducing its transcriptional activity and protein stability. Moreover, we reveal that PAX1 plays dual roles in hESC-derived definitive and foregut/pharyngeal endoderm cells, which give rise to the thymus epithelium, by inhibiting Wnt signaling. Importantly, our data show PAX1 mutations found in SCID patients significantly compromise the suppressing ability of PAX1 on Wnt signaling.ConclusionsOur study presents a novel molecular mode of action of PAX1 in regulation of canonical Wnt signaling and endoderm differentiation, thus providing insights for the molecular basis of PAX1 associated SCID, offering better understanding of the behavior of PAX1 in embryogenesis.

Unravelling the factors of evolutionary repeatability: insights and perspectives on predictability in evolutionary biology

Evolutionary biology was previously considered a historical science with predictions about evolutionary trajectories believed to be near impossible. The development of high throughput sequencing and data analysis technologies has challenged this belief, and provided an abundance of data that yields novel insights into evolutionary processes. Evolutionary predictions are now increasingly being used to develop fundamental knowledge of evolving systems and/or to demonstrate evolutionary control. Here we investigate the factors that make evolutionary repeatability more or less likely to increase the accuracy of evolutionary predictions. We identify outstanding questions and provide a potential starting point to determine how evolutionary repeatability is affected by genetic relatedness.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, ‘what you put is what you get’ (WYPIWYG) – that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Tools and methods for high-throughput single-cell imaging with the mother machine.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Microbial community structures and bacteria-Cylindrospermopsis raciborskii interactions in Yilong Lake.

Cylindrospermopsis raciborskii-dominated harmful algae blooms have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii-blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world.

Unveiling the epigenetic landscape of plants using flow cytometry approach.

Plants are sessile creatures that have to adapt constantly changing environmental circumstances. Plants are subjected to a range of abiotic stressors as a result of unpredictable climate change. Understanding how stress-responsive genes are regulated can help us better understand how plants can adapt to changing environmental conditions. Epigenetic markers that dynamically change in response to stimuli, such as DNA methylation and histone modifications are known to regulate gene expression. Individual cells or particles' physical and/or chemical properties can be measured using the method known as flow cytometry. It may therefore be used to evaluate changes in DNA methylation, histone modifications, and other epigenetic markers, making it a potent tool for researching epigenetics in plants. We explore the use of flow cytometry as a technique for examining epigenetic traits in this thorough discussion. The separation of cell nuclei and their subsequent labeling with fluorescent antibodies, offering information on the epigenetic mechanisms in plants when utilizing flow cytometry. We also go through the use of high-throughput data analysis methods to unravel the complex epigenetic processes occurring inside plant systems.

Distribution of Plasmopara viticola Causing Downy Mildew in Russian Far East Grapevines

Downy mildew is a severe disease that leads to significant losses in grape yields worldwide. It is caused by the oomycete Plasmopara viticola. The study of the distribution of this agent and the search for endophytic organisms that inhibit the growth of P. viticola are essential objectives to facilitate the transition to sustainable and high-yield agriculture, while respecting the environment. In this study, high-throughput sequencing of the ITS (ITS1f/ITS2 region) and 16S (V4 region) amplicons was employed to analyze 80 samples of leaves and stems from different grapevine species and cultivars grown in the Russian Far East (Vitis amurensis Rupr., Vitis coignetiae Pulliat, and several grapevine cultivars). The analysis revealed the presence of P. viticola in 53.75% of the grape samples. The pathogen P. viticola was not detected in V. amurensis samples collected near Vladivostok and Russky Island. Among the P. viticola-affected samples, only two (out of the eighty analyzed grape samples) from the Makarevich vineyard in Primorsky Krai exhibited disease symptoms, while the majority appeared visually healthy. We also found six distinct P. viticola ASVs in our metagenomic data. Based on phylogenetic analysis, we hypothesize that the P. viticola population in the Russian Far East may have originated from the invasive P. viticola clade aestivalis, which has spread around the world from North America. To identify putative microbial antagonists of P. viticola, a differential analysis of high-throughput sequencing data was conducted using the DESeq2 method to compare healthy and P. viticola-affected samples. The in silico analysis revealed an increased representation of certain taxa in healthy samples compared to P. viticola-affected ones: fungi—Kabatina sp., Aureobasidium sp., and Vishniacozyma sp.; bacteria—Hymenobacter spp., Sphingomonas spp., Massilia spp., Methylobacterium-Methylorubrum spp., and Chryseobacterium spp. This in-silico-obtained information on the potential microbial antagonists of P. viticola serves as a theoretical basis for the development of biocontrol agents for grapevine downy mildew.

Optimising high-throughput sequencing data analysis, from gene database selection to the analysis of compositional data: a case study on tropical soil nematodes

Optimising high-throughput sequencing data analysis, from gene database selection to the analysis of compositional data: a case study on tropical soil nematodes

The occurrence and development of abdominal aortic aneurysm may be related to the energy metabolism disorder and local inflammation

BackgroundThe cellular mechanism of the formation of abdominal aortic aneurysm (AAA) is very complicated. A series of sophisticated events eventually led to significant pathological changes in the anatomical structure and function of the arterial wall and they are still not clear nowadays. MethodsWe pooled publicly available GEO datasets (GSE57691 and GSE47472) to get a comprehensive comparisons between normal tissues and AAA tissues to try to reveal molecular mechanism underlying the disease. Total 63 AAA samples and 18 normal tissue samples were compared and we fond that there were 784 significantly different gene (DEGs, threshold set as adjusted P < 0.05 and Log FC < 1) were identified. At the same time, we validate the possible signaling factor expression of AAA by comparing the normal tissue of the human body with the AAA tissue. ResultsIn the pathway enrichment, we found that FOXP3 related signaling pathways, inflammation-related cytokine signaling pathways, interleukin-8-CXCR1 related signaling pathways and VEGFA and FGFR1 related signal pathway were significantly enrichmented. In Weighted gene co-expression network analysis (WGCNA), we found that the key hub genes were significantly related to lipid catabolic metabolism, which further verified the possibility that AAA might relate to energy metabolism disorders. ConclusionBased on the comprehensive analysis of previous high-throughput data and the validation of basic experiments, we found that the occurrence of AAA may be related to energy metabolism disorders and local inflammation.

Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies.

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.

Highly scalable and standardized organ-on-chip platform with TEER for biological barrier modeling

ABSTRACT The development of new therapies is hampered by the lack of predictive, and patient-relevant in vitro models. Organ-on-chip (OOC) technologies can potentially recreate physiological features and hold great promise for tissue and disease modeling. However, the non-standardized design of these chips and perfusion control systems has been a barrier to quantitative high-throughput screening (HTS). Here we present a scalable OOC microfluidic platform for applied kinetic in vitro assays (AKITA) that is applicable for high, medium, and low throughput. Its standard 96-well plate and 384-well plate layouts ensure compatibility with existing laboratory workflows and high-throughput data collection and analysis tools. The AKITA plate is optimized for the modeling of vascularized biological barriers, primarily the blood-brain barrier, skin, and lung, with precise flow control on a custom rocker. The integration of trans-epithelial electrical resistance (TEER) sensors allows rapid and repeated monitoring of barrier integrity over long time periods. Together with automated liquid handling and compound permeability testing analyses, we demonstrate the flexibility of the AKITA platform for establishing human-relevant models for preclinical drug and precision medicine’s efficacy, toxicity, and permeability under near-physiological conditions.

Identification of BANF1 as a novel prognostic biomarker in gastric cancer and validation via in-vitro and in-vivo experiments.

Gastric cancer (GC) is a widespread malignancy characterized by a notably high incidence rate and an unfavorable prognosis. We conducted a meticulous analysis of GC high-throughput sequencing data downloaded from the Gene Expression Omnibus (GEO) repository to pinpoint distinctive genes associated with GC. Our investigation successfully identified three signature genes implicated in GC, with a specific focus on the barrier to autointegration factor 1 (BANF1), which exhibits elevated expression across various cancer types, including GC. Bioinformatic analysis has highlighted BANF1 as a prognostic indicator for patients with GC, with direct implications for immune cell infiltration. To gain a more comprehensive understanding of the significance of BANF1 in GC, we performed a series of in vitro experiments to confirm its high expression in GC tissues and cellular components. Intriguingly, the induction of BANF1 knockdown resulted in a marked attenuation of proliferation, migratory capacity, and invasive potential in GC cells. Moreover, our in vivo experiments using nude mouse models revealed a notable impediment in tumor growth following BANF1 knockdown. These insights underscore the feasibility of BANF1 as a novel therapeutic target for GC.

Methods: A bioinformatic protocol for rapid analysis of zebrafish embryo photo-motory responses (PMR) in neurotoxicity testing

Chemobehavioural phenotyping presents unique opportunities for analyzing neurotoxicants and discovering behavior-modifying neuroceuticals in small aquatic model organisms such as zebrafish (Danio rerio). A recently popularized approach in this field involves the utilization of zebrafish embryos for a photo-motor response (PMR) bioassay. The PMR bioassay entails stimulating zebrafish embryos between 24 and 36 h post fertilization (hpf) with a high-intensity light stimulus, inducing a transient increase in the frequency of photo-induced embryo body flexions. These flexions can be computationally analyzed to derive behavioral signatures, enabling the categorization of neuromodulating chemicals.Despite the significant advantages of the PMR bioassay, its widespread implementation is hindered by lack of well described and straightforward high-throughput bioinformatic analysis of behavioral data. In this methods article, we present an easily implementable bioinformatics protocol specifically designed for rapid behavioral analysis of large cohorts of zebrafish specimens in PMR bioassays. We also address common pitfalls encountered during PMR analysis, discuss its limitations, and propose future directions for developing next-generation biometric analysis techniques in chemobehavioural assays utilizing zebrafish embryos.

ExpoSeq: simplified analysis of high-throughput sequencing data from antibody discovery campaigns.

High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil the full potential of display techniques, like antibody phage display, in molecular biology. However, a significant challenge lies in the processing and analysis of such data. Furthermore, there is a notable absence of open-access user-friendly software tools that can be utilized by scientists lacking programming expertise. Here, we present ExpoSeq as an easy-to-use tool to explore, process, and visualize HTS data from antibody discovery campaigns like an expert while only requiring a beginner's knowledge. The pipeline is distributed via GitHub and PyPI, and it can either be installed as a package with pip or the user can choose to clone the repository.

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19.

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.